// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#ifndef EIGEN_CXX11_TENSOR_TENSOR_FORWARD_DECLARATIONS_H
#define EIGEN_CXX11_TENSOR_TENSOR_FORWARD_DECLARATIONS_H

namespace Eigen {

// MakePointer class is used as a container of the address space of the pointer
// on the host and on the device. From the host side it generates the T* pointer
// and when EIGEN_USE_SYCL is used it construct a buffer with a map_allocator to
// T* m_data on the host. It is always called on the device.
// Specialisation of MakePointer class for creating the sycl buffer with
// map_allocator.
template<typename T>
struct MakePointer
{
	typedef T* Type;
	typedef const T* ConstType;
};

template<typename T>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T*
constCast(const T* data)
{
	return const_cast<T*>(data);
}

// The StorageMemory class is a container of the device specific pointer
// used for refering to a Pointer on TensorEvaluator class. While the TensorExpression
// is a device-agnostic type and need MakePointer class for type conversion,
// the TensorEvaluator class can be specialized for a device, hence it is possible
// to construct different types of temproray storage memory in TensorEvaluator
// for different devices by specializing the following StorageMemory class.
template<typename T, typename device>
struct StorageMemory : MakePointer<T>
{};

namespace internal {
template<typename A, typename B>
struct Pointer_type_promotion
{
	static const bool val = false;
};
template<typename A>
struct Pointer_type_promotion<A, A>
{
	static const bool val = true;
};
template<typename A, typename B>
struct TypeConversion
{
	typedef A* type;
};
}

template<typename PlainObjectType, int Options_ = Unaligned, template<class> class MakePointer_ = MakePointer>
class TensorMap;
template<typename Scalar_, int NumIndices_, int Options_ = 0, typename IndexType = DenseIndex>
class Tensor;
template<typename Scalar_, typename Dimensions, int Options_ = 0, typename IndexType = DenseIndex>
class TensorFixedSize;
template<typename PlainObjectType>
class TensorRef;
template<typename Derived, int AccessLevel>
class TensorBase;

template<typename NullaryOp, typename PlainObjectType>
class TensorCwiseNullaryOp;
template<typename UnaryOp, typename XprType>
class TensorCwiseUnaryOp;
template<typename BinaryOp, typename LeftXprType, typename RightXprType>
class TensorCwiseBinaryOp;
template<typename TernaryOp, typename Arg1XprType, typename Arg2XprType, typename Arg3XprType>
class TensorCwiseTernaryOp;
template<typename IfXprType, typename ThenXprType, typename ElseXprType>
class TensorSelectOp;
template<typename Op, typename Dims, typename XprType, template<class> class MakePointer_ = MakePointer>
class TensorReductionOp;
template<typename XprType>
class TensorIndexTupleOp;
template<typename ReduceOp, typename Dims, typename XprType>
class TensorTupleReducerOp;
template<typename Axis, typename LeftXprType, typename RightXprType>
class TensorConcatenationOp;
template<typename Dimensions, typename LeftXprType, typename RightXprType, typename OutputKernelType>
class TensorContractionOp;
template<typename TargetType, typename XprType>
class TensorConversionOp;
template<typename Dimensions, typename InputXprType, typename KernelXprType>
class TensorConvolutionOp;
template<typename FFT, typename XprType, int FFTDataType, int FFTDirection>
class TensorFFTOp;
template<typename PatchDim, typename XprType>
class TensorPatchOp;
template<DenseIndex Rows, DenseIndex Cols, typename XprType>
class TensorImagePatchOp;
template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType>
class TensorVolumePatchOp;
template<typename Broadcast, typename XprType>
class TensorBroadcastingOp;
template<DenseIndex DimId, typename XprType>
class TensorChippingOp;
template<typename NewDimensions, typename XprType>
class TensorReshapingOp;
template<typename XprType>
class TensorLayoutSwapOp;
template<typename StartIndices, typename Sizes, typename XprType>
class TensorSlicingOp;
template<typename ReverseDimensions, typename XprType>
class TensorReverseOp;
template<typename PaddingDimensions, typename XprType>
class TensorPaddingOp;
template<typename Shuffle, typename XprType>
class TensorShufflingOp;
template<typename Strides, typename XprType>
class TensorStridingOp;
template<typename StartIndices, typename StopIndices, typename Strides, typename XprType>
class TensorStridingSlicingOp;
template<typename Strides, typename XprType>
class TensorInflationOp;
template<typename Generator, typename XprType>
class TensorGeneratorOp;
template<typename LeftXprType, typename RightXprType>
class TensorAssignOp;
template<typename Op, typename XprType>
class TensorScanOp;
template<typename Dims, typename XprType>
class TensorTraceOp;

template<typename CustomUnaryFunc, typename XprType>
class TensorCustomUnaryOp;
template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType>
class TensorCustomBinaryOp;

template<typename XprType, template<class> class MakePointer_ = MakePointer>
class TensorEvalToOp;
template<typename XprType>
class TensorForcedEvalOp;

template<typename ExpressionType, typename DeviceType>
class TensorDevice;
template<typename ExpressionType, typename DeviceType, typename DoneCallback>
class TensorAsyncDevice;
template<typename Derived, typename Device>
struct TensorEvaluator;

struct NoOpOutputKernel;

struct DefaultDevice;
struct ThreadPoolDevice;
struct GpuDevice;
struct SyclDevice;

#ifdef EIGEN_USE_SYCL

template<typename T>
struct MakeSYCLPointer
{
	typedef Eigen::TensorSycl::internal::RangeAccess<cl::sycl::access::mode::read_write, T> Type;
};

template<typename T>
EIGEN_STRONG_INLINE const Eigen::TensorSycl::internal::RangeAccess<cl::sycl::access::mode::read_write, T>&
constCast(const Eigen::TensorSycl::internal::RangeAccess<cl::sycl::access::mode::read_write, T>& data)
{
	return data;
}

template<typename T>
struct StorageMemory<T, SyclDevice> : MakeSYCLPointer<T>
{};
template<typename T>
struct StorageMemory<T, const SyclDevice> : StorageMemory<T, SyclDevice>
{};

namespace TensorSycl {
namespace internal {
template<typename Evaluator, typename Op>
class GenericNondeterministicReducer;
}
}
#endif

enum FFTResultType
{
	RealPart = 0,
	ImagPart = 1,
	BothParts = 2
};

enum FFTDirection
{
	FFT_FORWARD = 0,
	FFT_REVERSE = 1
};

namespace internal {

template<typename Device, typename Expression>
struct IsVectorizable
{
	static const bool value = TensorEvaluator<Expression, Device>::PacketAccess;
};

template<typename Expression>
struct IsVectorizable<GpuDevice, Expression>
{
	static const bool value =
		TensorEvaluator<Expression, GpuDevice>::PacketAccess && TensorEvaluator<Expression, GpuDevice>::IsAligned;
};

// Tiled evaluation strategy.
enum TiledEvaluation
{
	Off = 0, // tiled evaluation is not supported
	On = 1,	 // still work in progress (see TensorBlock.h)
};

template<typename Device, typename Expression>
struct IsTileable
{
	// Check that block evaluation is supported and it's a preferred option (at
	// least one sub-expression has much faster block evaluation, e.g.
	// broadcasting).
	static const bool BlockAccess =
		TensorEvaluator<Expression, Device>::BlockAccess && TensorEvaluator<Expression, Device>::PreferBlockAccess;

	static const TiledEvaluation value = BlockAccess ? TiledEvaluation::On : TiledEvaluation::Off;
};

template<typename Expression,
		 typename Device,
		 bool Vectorizable = IsVectorizable<Device, Expression>::value,
		 TiledEvaluation Tiling = IsTileable<Device, Expression>::value>
class TensorExecutor;

template<typename Expression,
		 typename Device,
		 typename DoneCallback,
		 bool Vectorizable = IsVectorizable<Device, Expression>::value,
		 TiledEvaluation Tiling = IsTileable<Device, Expression>::value>
class TensorAsyncExecutor;

} // end namespace internal

} // end namespace Eigen

#endif // EIGEN_CXX11_TENSOR_TENSOR_FORWARD_DECLARATIONS_H
